import Mathlib namespace OAI section open MeasureTheory ProbabilityTheory InformationTheory Set Matrix open scoped ENNReal NNReal RealInnerProductSpace Classical noncomputable section namespace PosteriorReplicas abbrev Vec (d : ℕ) := EuclideanSpace ℝ (Fin d) abbrev Rows (d k : ℕ) := Fin k → Vec d abbrev Labels (k : ℕ) := Fin k → ℝ abbrev Data (d k : ℕ) := Rows d k × Labels k abbrev Tuple (d m : ℕ) := Fin (m + 1) → Vec d def sphereLaw (d : ℕ) : Measure (Vec d) := let σ := (volume : Measure (Vec d)).toSphere ((σ univ)⁻¹ • σ).map Subtype.val def gaussianRows (d k : ℕ) : Measure (Rows d k) := Measure.pi fun _ => stdGaussian (Vec d) instance (d k : ℕ) : IsProbabilityMeasure (gaussianRows d k) := by unfold gaussianRows infer_instance def labels {d k : ℕ} (A : Rows d k) (s : Vec d) : Labels k := fun i => ⟪A i, s⟫ lemma measurable_labels {d k : ℕ} : Measurable (fun z : Rows d k × Vec d => labels z.1 z.2) := by unfold labels fun_prop def messageKernel {d k N : ℕ} (κ : Kernel (Data d k) (Fin N)) : Kernel (Vec d × Rows d k) (Fin N) := κ.comap (fun z => (z.2, labels z.2 z.1)) (by unfold labels fun_prop) instance {d k N : ℕ} (κ : Kernel (Data d k) (Fin N)) [IsMarkovKernel κ] : IsMarkovKernel (messageKernel κ) := by unfold messageKernel infer_instance def signalMessageLaw {d k N : ℕ} (p : Measure (Vec d)) (κ : Kernel (Data d k) (Fin N)) : Measure (Vec d × Fin N) := ((p.prod (gaussianRows d k)) ⊗ₘ messageKernel κ).map (fun z => (z.1.1, z.2)) instance {d k N : ℕ} (p : Measure (Vec d)) [IsProbabilityMeasure p] (κ : Kernel (Data d k) (Fin N)) [IsMarkovKernel κ] : IsProbabilityMeasure (signalMessageLaw p κ) := by unfold signalMessageLaw infer_instance def sideLaw {d N : ℕ} (ℓ : ℕ) (q : Measure (Vec d × Fin N)) : Measure (Data d ℓ × (Vec d × Fin N)) := ((gaussianRows d ℓ).prod q).map (fun z => ((z.1, labels z.1 z.2.1), z.2)) instance {d N : ℕ} (ℓ : ℕ) (q : Measure (Vec d × Fin N)) [IsProbabilityMeasure q] : IsProbabilityMeasure (sideLaw ℓ q) := by unfold sideLaw infer_instance def messageEntropy {d N : ℕ} (q : Measure (Vec d × Fin N)) : ℝ := ∑ w : Fin N, -(q.snd.real {w}) * Real.log (q.snd.real {w}) def conditionalInformation {d ℓ N : ℕ} [NeZero N] (μ : Measure (Data d ℓ × (Vec d × Fin N))) [IsFiniteMeasure μ] : ℝ≥0∞ := klDiv μ (μ.fst ⊗ₘ (μ.condKernel.fst ×ₖ μ.condKernel.snd)) def GaussianReplicaBlock : Prop := ∃ C : ℝ, 0 < C ∧ ∃ d₀ : ℕ, ∀ d : ℕ, d₀ ≤ d → let k := 2 * (d / 16) let t := k + 1 let ℓ := 4 * k ∀ (p : Measure (Vec d)) [IsProbabilityMeasure p] (f : Vec d → ℝ) (L : ℝ), Measurable f → (∀ x, 0 ≤ f x ∧ f x ≤ L) → p = (sphereLaw d).withDensity (fun x => ENNReal.ofReal (f x)) → ∀ (N : ℕ) [NeZero N] (κ : Kernel (Data d k) (Fin N)) [IsMarkovKernel κ], conditionalInformation (sideLaw ℓ (signalMessageLaw p κ)) ≤ ENNReal.ofReal (messageEntropy (signalMessageLaw p κ) / (t : ℝ) + C * (d : ℝ) + C * Real.log (2 + Real.log L)) inductive BaseLaw where | spherical | gaussian deriving DecidableEq def BaseLaw.measure (base : BaseLaw) (d : ℕ) : Measure (Vec d) := match base with | .spherical => sphereLaw d | .gaussian => stdGaussian (Vec d) def rowGram {d k : ℕ} (A : Rows d k) : Matrix (Fin k) (Fin k) ℝ := fun i j => ⟪A i, A j⟫ def liftLabel {d k : ℕ} (A : Rows d k) (y : Labels k) : Vec d := ∑ i, ((rowGram A)⁻¹ *ᵥ y) i • A i def kernelComponent {d k : ℕ} (A : Rows d k) (x : Vec d) : Vec d := x - liftLabel A (labels A x) def baseFiber {d k : ℕ} (base : BaseLaw) (A : Rows d k) (y : Labels k) : Measure (Vec d) := match base with | .gaussian => (stdGaussian (Vec d)).map (fun x => liftLabel A y + kernelComponent A x) | .spherical => (stdGaussian (Vec d)).map (fun x => liftLabel A y + (Real.sqrt (1 - ‖liftLabel A y‖ ^ 2) / ‖kernelComponent A x‖) • kernelComponent A x) def baseDensity {d k : ℕ} (base : BaseLaw) (A : Rows d k) (y : Labels k) : ℝ≥0∞ := if LinearIndependent ℝ A then match base with | .gaussian => ENNReal.ofReal ((2 * Real.pi) ^ (-(k : ℝ) / 2) * Real.exp (-‖liftLabel A y‖ ^ 2 / 2) / Real.sqrt (rowGram A).det) | .spherical => if ‖liftLabel A y‖ < 1 then ENNReal.ofReal (Real.Gamma ((d : ℝ) / 2) / (Real.pi ^ ((k : ℝ) / 2) * Real.Gamma (((d : ℝ) - k) / 2)) * (1 - ‖liftLabel A y‖ ^ 2) ^ (((d : ℝ) - k - 2) / 2) / Real.sqrt (rowGram A).det) else 0 else 0 def tiltMass {d k : ℕ} (base : BaseLaw) (f : Vec d → ℝ≥0∞) (A : Rows d k) (y : Labels k) : ℝ≥0∞ := ∫⁻ x, f x ∂baseFiber base A y def projectedDensity {d k : ℕ} (base : BaseLaw) (f : Vec d → ℝ≥0∞) (A : Rows d k) (y : Labels k) : ℝ≥0∞ := baseDensity base A y * tiltMass base f A y def tiltedFiber {d k : ℕ} (base : BaseLaw) (f : Vec d → ℝ≥0∞) (A : Rows d k) (y : Labels k) : Measure (Vec d) := if tiltMass base f A y = 0 then Measure.dirac 0 else (tiltMass base f A y)⁻¹ • (baseFiber base A y).withDensity f def differences {d m : ℕ} (u : Tuple d m) : Fin m → Vec d := fun i => u i.succ - u 0 def differenceSpan {d m : ℕ} (u : Tuple d m) : Submodule ℝ (Vec d) := Submodule.span ℝ (Set.range (differences u)) def affineVolume {d m : ℕ} (u : Tuple d m) : ℝ := Real.sqrt (Matrix.gram ℝ (differences u)).det def constrainedRows {d m : ℕ} (k : ℕ) (u : Tuple d m) : Measure (Rows d k) := (gaussianRows d k).map (fun A i => (differenceSpan u)ᗮ.starProjection (A i)) def equalLabelLeft {d k m : ℕ} (base : BaseLaw) (f : Vec d → ℝ≥0∞) (H : Rows d k × Labels k × Tuple d m → ℝ≥0∞) : ℝ≥0∞ := ∫⁻ A, ∫⁻ y, (projectedDensity base f A y) ^ (m + 1) * ∫⁻ u, H (A, y, u) ∂Measure.pi (fun _ : Fin (m + 1) => tiltedFiber base f A y) ∂volume ∂gaussianRows d k def equalLabelRight {d k m : ℕ} (base : BaseLaw) (f : Vec d → ℝ≥0∞) (H : Rows d k × Labels k × Tuple d m → ℝ≥0∞) : ℝ≥0∞ := ENNReal.ofReal ((2 * Real.pi) ^ (-(k * m : ℝ) / 2)) * ∫⁻ u, (ENNReal.ofReal (affineVolume u))⁻¹ ^ k * ∫⁻ A, H (A, labels A (u 0), u) ∂constrainedRows k u ∂Measure.pi (fun _ : Fin (m + 1) => (base.measure d).withDensity f) def EqualLabelMeasureIdentity : Prop := ∀ (d k m : ℕ), 1 ≤ k → 1 ≤ m → k + m ≤ d - 2 → ∀ (base : BaseLaw) (f : Vec d → ℝ≥0∞), Measurable f → (∃ L : ℝ≥0∞, L ≠ ∞ ∧ ∀ x, f x ≤ L) → equalLabelLeft (k := k) (m := m) base f (fun _ => 1) < ∞ ∧ ∀ H : Rows d k × Labels k × Tuple d m → ℝ≥0∞, Measurable H → equalLabelLeft base f H = equalLabelRight base f H def DistanceReplicaBlock : Prop := ∃ C : ℝ, 0 < C ∧ ∃ d₀ : ℕ, ∀ d : ℕ, d₀ ≤ d → ∀ (p : Measure (Vec d)) [IsProbabilityMeasure p] (f : Vec d → ℝ) (b : ℝ), 0 ≤ b → Measurable f → (∀ x, 0 ≤ f x ∧ f x ≤ Real.exp b) → p = (sphereLaw d).withDensity (fun x => ENNReal.ofReal (f x)) → ∀ (N : ℕ) [NeZero N] (κ : Kernel (Data d (d / 16)) (Fin N)) [IsMarkovKernel κ], conditionalInformation (sideLaw (d / 2) (signalMessageLaw p κ)) ≤ ENNReal.ofReal (messageEntropy (signalMessageLaw p κ) / ((d / 8 : ℕ) : ℝ) + C * ((d : ℝ) + b / (d : ℝ))) def SyntheticReplicaBlock : Prop := ∃ C : ℝ, 0 < C ∧ ∀ d : ℕ, 16 ≤ d → ∀ (p : Measure (Vec d)) [IsProbabilityMeasure p] (f : Vec d → ℝ) (L : ℝ), 1 ≤ L → Measurable f → (∀ x, 0 ≤ f x ∧ f x ≤ L) → p = (sphereLaw d).withDensity (fun x => ENNReal.ofReal (f x)) → ∀ (N : ℕ) [NeZero N] (z : Data d (d / 8) → Fin N) (hz : Measurable z), conditionalInformation (sideLaw (d / 2) (signalMessageLaw p (Kernel.deterministic z hz))) ≤ ENNReal.ofReal (Real.log (N : ℝ) / ((d / 8 : ℕ) : ℝ) + C * ((d : ℝ) + Real.log (1 + Real.log L))) def IncidenceReplicaBlock : Prop := ∃ C : ℝ, 0 < C ∧ ∃ d₀ : ℕ, ∀ d : ℕ, d₀ ≤ d → ∀ (p : Measure (Vec d)) [IsProbabilityMeasure p] (f : Vec d → ℝ) (F : ℝ), 1 ≤ F → Measurable f → (∀ x, 0 ≤ f x ∧ f x ≤ F) → p = (sphereLaw d).withDensity (fun x => ENNReal.ofReal (f x)) → ∀ (N : ℕ) [NeZero N] (κ : Kernel (Data d (d / 16)) (Fin N)) [IsMarkovKernel κ], conditionalInformation (sideLaw (d / 4) (signalMessageLaw p κ)) ≤ ENNReal.ofReal (Real.log (N : ℝ) / ((d / 16 : ℕ) : ℝ) + C * (d : ℝ) + C * Real.log (1 + Real.log F)) def labelNorm {k : ℕ} (y : Labels k) : ℝ := ‖WithLp.toLp 2 y‖ def ballDensityVersion {d k : ℕ} (ν : Measure (Vec d)) (A : Rows d k) (y : Labels k) : ℝ≥0∞ := Filter.liminf (fun n : ℕ => ((volume : Measure (Labels k)) {v | labelNorm v < (n + 1 : ℝ)⁻¹})⁻¹ * ν {x | labelNorm (labels A x - y) < (n + 1 : ℝ)⁻¹}) Filter.atTop def gaussianDensityVersion {d k : ℕ} (ν : Measure (Vec d)) (A : Rows d k) (y : Labels k) : ℝ≥0∞ := Filter.liminf (fun n : ℕ => ∫⁻ x, ENNReal.ofReal ((2 * Real.pi * ((n + 1 : ℝ)⁻¹)^2) ^ (-(k : ℝ) / 2) * Real.exp (-(labelNorm (labels A x - y))^2 / (2 * ((n + 1 : ℝ)⁻¹)^2))) ∂ν) Filter.atTop def EqualLabelVersions : Prop := ∀ (d k m : ℕ), 1 ≤ k → 1 ≤ m → k + m ≤ d - 2 → ∀ (base : BaseLaw) (f : Vec d → ℝ≥0∞), Measurable f → (∃ L : ℝ≥0∞, L ≠ ∞ ∧ ∀ x, f x ≤ L) → let ν := (base.measure d).withDensity f (Measurable (fun z : Rows d k × Labels k => projectedDensity base f z.1 z.2)) ∧ (Measurable (fun z : Rows d k × Labels k => ballDensityVersion ν z.1 z.2)) ∧ (Measurable (fun z : Rows d k × Labels k => gaussianDensityVersion ν z.1 z.2)) ∧ (∀ A : Rows d k, LinearIndependent ℝ A → ν.map (labels A) = volume.withDensity (projectedDensity base f A) ∧ projectedDensity base f A =ᵐ[volume] ballDensityVersion ν A ∧ projectedDensity base f A =ᵐ[volume] gaussianDensityVersion ν A) ∧ (∀ᵐ u ∂Measure.pi (fun _ : Fin (m + 1) => ν), LinearIndependent ℝ (differences u) ∧ ∀ᵐ A ∂constrainedRows k u, LinearIndependent ℝ A ∧ (∀ i, labels A (u i) = labels A (u 0)) ∧ (base = .spherical → ‖liftLabel A (labels A (u 0))‖ < 1) ∧ 0 < projectedDensity base f A (labels A (u 0)) ∧ projectedDensity base f A (labels A (u 0)) ≠ ∞ ∧ projectedDensity base f A (labels A (u 0)) = ballDensityVersion ν A (labels A (u 0)) ∧ projectedDensity base f A (labels A (u 0)) = gaussianDensityVersion ν A (labels A (u 0))) def haarRows (d ℓ : ℕ) : Measure (Rows d ℓ) := (gaussianRows d ℓ).map (InnerProductSpace.gramSchmidtNormed ℝ) def cmi {Z X Y : Type*} [MeasurableSpace Z] [MeasurableSpace X] [MeasurableSpace Y] [StandardBorelSpace X] [StandardBorelSpace Y] [Nonempty X] [Nonempty Y] (μ : Measure (Z × (X × Y))) [IsFiniteMeasure μ] : ℝ≥0∞ := klDiv μ (μ.fst ⊗ₘ (μ.condKernel.fst ×ₖ μ.condKernel.snd)) def finiteEntropy {J : Type*} [Fintype J] [MeasurableSpace J] (μ : Measure J) : ℝ := ∑ j, - μ.real {j} * Real.log (μ.real {j}) def stateMessageKernel {d k V W : ℕ} (κ : Kernel (Fin V × Data d k) (Fin W)) : Kernel ((Vec d × Fin V) × Rows d k) (Fin W) := κ.comap (fun z => (z.1.2, (z.2, labels z.2 z.1.1))) (by unfold labels; fun_prop) instance {d k V W : ℕ} (κ : Kernel (Fin V × Data d k) (Fin W)) [IsMarkovKernel κ] : IsMarkovKernel (stateMessageKernel κ) := by unfold stateMessageKernel infer_instance def stateMessageLaw {d k V W : ℕ} (q : Measure (Vec d × Fin V)) (κ : Kernel (Fin V × Data d k) (Fin W)) : Measure ((Vec d × Fin V) × Fin W) := (((q.prod (gaussianRows d k)) ⊗ₘ stateMessageKernel κ).map (fun z => (z.1.1, z.2))) instance {d k V W : ℕ} (q : Measure (Vec d × Fin V)) [IsProbabilityMeasure q] (κ : Kernel (Fin V × Data d k) (Fin W)) [IsMarkovKernel κ] : IsProbabilityMeasure (stateMessageLaw q κ) := by unfold stateMessageLaw infer_instance def stateHaarSideLaw {d V W : ℕ} (ℓ : ℕ) (q : Measure ((Vec d × Fin V) × Fin W)) : Measure ((Fin V × Data d ℓ) × (Vec d × Fin W)) := ((haarRows d ℓ).prod q).map (fun z => ((z.2.1.2, (z.1, labels z.1 z.2.1.1)), (z.2.1.1, z.2.2))) instance {d V W : ℕ} (ℓ : ℕ) (q : Measure ((Vec d × Fin V) × Fin W)) [IsFiniteMeasure q] : IsFiniteMeasure (stateHaarSideLaw ℓ q) := by unfold stateHaarSideLaw haarRows infer_instance def terminalConditionalEntropy {d V W : ℕ} (q : Measure ((Vec d × Fin V) × Fin W)) : ℝ := finiteEntropy (q.map (fun z => (z.1.2, z.2))) - finiteEntropy (q.map (fun z => z.1.2)) def HaarReplicaBlock : Prop := ∃ C : ℝ, 0 < C ∧ ∀ d : ℕ, 64 ≤ d → ∀ (V W : ℕ) [NeZero V] [NeZero W] (q : Measure (Vec d × Fin V)) [IsProbabilityMeasure q], q.fst = sphereLaw d → ∀ (κ : Kernel (Fin V × Data d (d / 16)) (Fin W)) [IsMarkovKernel κ], cmi (stateHaarSideLaw (d / 4) (stateMessageLaw q κ)) ≤ ENNReal.ofReal (terminalConditionalEntropy (stateMessageLaw q κ) / ((d / 16 + 1 : ℕ) : ℝ) + C * (d : ℝ) + Real.log (1 + C * (d : ℝ) + finiteEntropy q.snd) + C * (finiteEntropy q.snd + Real.log (d : ℝ)) / (d : ℝ)^2) end PosteriorReplicas end end open MeasureTheory ProbabilityTheory Set Filter open scoped ENNReal NNReal RealInnerProductSpace noncomputable section universe u namespace PosteriorRegression abbrev Vector (d : ℕ) := EuclideanSpace ℝ (Fin d) abbrev UnitSphere (d : ℕ) := Metric.sphere (0 : Vector d) 1 abbrev State (M : ℕ) := Fin (2 ^ M) abbrev Samples (d T : ℕ) := Fin T → Vector d def uniformSphere (d : ℕ) : Measure (UnitSphere d) := let σ := (volume : Measure (Vector d)).toSphere (σ Set.univ)⁻¹ • σ def sampleLaw (d T : ℕ) : Measure (Samples d T) := Measure.pi fun _ => stdGaussian (Vector d) def observationReference (d : ℕ) : Measure (Vector d × ℝ) := (stdGaussian (Vector d)).prod volume namespace Learner abbrev RunStatus (M : ℕ) := Sum (State M) (ℕ × State M) def angularError {d : ℕ} (s ŝ : UnitSphere d) : ℝ := Real.arccos ⟪ŝ.val, s.val⟫ end Learner end PosteriorRegression open MeasureTheory ProbabilityTheory InformationTheory Set Matrix open scoped ENNReal NNReal RealInnerProductSpace Classical noncomputable section namespace PosteriorRegression abbrev CompletedObservation (d : ℕ) := NullMeasurableSpace (Vector d × ℝ) (observationReference d) abbrev Destination (M : ℕ) := Bool × State M structure CompletedKernelLearner (d M : ℕ) where initial : Measure (Destination M) initial_probability : IsProbabilityMeasure initial transition : ℕ → State M → Kernel (CompletedObservation d) (Destination M) transition_markov : ∀ t a, IsMarkovKernel (transition t a) output : ℕ → State M → Measure (UnitSphere d) output_probability : ∀ t a, IsProbabilityMeasure (output t a) attribute [instance] CompletedKernelLearner.initial_probability CompletedKernelLearner.transition_markov CompletedKernelLearner.output_probability namespace CompletedKernelLearner open Learner def destStatus {M : ℕ} (n : ℕ) (v : Destination M) : RunStatus M := if v.1 then Sum.inr (n,v.2) else Sum.inl v.2 def finishState {M : ℕ} (T : ℕ) : RunStatus M → ℕ × State M | .inl a => (T,a) | .inr done => done def stepMeasure {d M T : ℕ} (K : CompletedKernelLearner d M) (s : UnitSphere d) (x : Samples d T) (i : Fin T) : RunStatus M → Measure (RunStatus M) | .inr done => Measure.dirac (.inr done) | .inl a => (K.transition (i.val+1) a (x i,⟪x i,s.val⟫)).map (destStatus (i.val+1)) def runMeasure {d M T : ℕ} (K : CompletedKernelLearner d M) (s : UnitSphere d) (x : Samples d T) : ℕ → Measure (RunStatus M) | 0 => K.initial.map (destStatus 0) | n+1 => if hn : n K.output a.1 a.2) end CompletedKernelLearner structure JointKernelExperiment (d M T : ℕ) (Ω : Type u) [MeasurableSpace Ω] (ρ : Measure Ω) where rules : Ω → CompletedKernelLearner d M outputKernel : Kernel (Ω × (UnitSphere d × Samples d T)) (UnitSphere d) output_markov : IsMarkovKernel outputKernel conditional_output : ∀ᵐ ω ∂ρ, ∀ᵐ z ∂((uniformSphere d).prod (sampleLaw d T)), outputKernel (ω,z) = (rules ω).estimateLaw z.1 z.2 attribute [instance] JointKernelExperiment.output_markov namespace JointKernelExperiment open Learner def law {d M T : ℕ} {Ω : Type u} [MeasurableSpace Ω] {ρ : Measure Ω} (E : JointKernelExperiment d M T Ω ρ) : Measure ((Ω × (UnitSphere d × Samples d T)) × UnitSphere d) := (ρ.prod ((uniformSphere d).prod (sampleLaw d T))) ⊗ₘ E.outputKernel def uniformSuccess {d M T : ℕ} {Ω : Type u} [MeasurableSpace Ω] {ρ : Measure Ω} (E : JointKernelExperiment d M T Ω ρ) (ε : ℝ) : ℝ≥0∞ := E.law {z | angularError z.1.2.1 z.2 ≤ ε} end JointKernelExperiment def KernelSubquadraticMemory : Prop := ∃ c : ℝ, 0 < c ∧ ∀ M : ℕ → ℕ, Asymptotics.IsLittleO atTop (fun d => (M d : ℝ)) (fun d => (d : ℝ)^2) → ∃ d₀ : ℕ, ∀ d : ℕ, d₀ ≤ d → ∀ T : ℕ, ∀ ε : ℝ, 0<ε → ε≤1/10 → ∀ (Ω : Type u) [MeasurableSpace Ω] (ρ : Measure Ω), IsProbabilityMeasure ρ → ∀ E : JointKernelExperiment d (M d) T Ω ρ, (2/3 : ℝ≥0∞) ≤ E.uniformSuccess ε → c * (d : ℝ) * Real.log (1/ε) ≤ (T : ℝ) end PosteriorRegression namespace PosteriorReplicas end PosteriorReplicas end end end OAI